Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis

Mobile telecommunication companies in Malaysia have been widely used in the recent decade. There is intense competition among them to keep and gain new customers by offering various services. The reviews of the services by the customers are commonly shared on social media such as Twitter. Those revi...

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Bibliographic Details
Published in:International Journal of Advanced Computer Science and Applications
Main Author: Rahim M.R.A.; Mahmud Y.; Abdul-Rahman S.
Format: Article
Language:English
Published: Science and Information Organization 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122571438&doi=10.14569%2fIJACSA.2021.0121229&partnerID=40&md5=377254d254df1e8f4c6905b314405c90
id 2-s2.0-85122571438
spelling 2-s2.0-85122571438
Rahim M.R.A.; Mahmud Y.; Abdul-Rahman S.
Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis
2021
International Journal of Advanced Computer Science and Applications
12
12
10.14569/IJACSA.2021.0121229
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122571438&doi=10.14569%2fIJACSA.2021.0121229&partnerID=40&md5=377254d254df1e8f4c6905b314405c90
Mobile telecommunication companies in Malaysia have been widely used in the recent decade. There is intense competition among them to keep and gain new customers by offering various services. The reviews of the services by the customers are commonly shared on social media such as Twitter. Those reviews are essential for mobile telecommunication companies to improve their services and at the same time to keep their customers from churning to another company. Hence, this study focuses on the public sentiment on Twitter towards mobile telecommunication services in Malaysia. Data on Twitter was scraped using three keywords: Celcom, Digi, and Maxis. The keywords used to refer to Malaysia's top three mobile telecommunication companies. The timeline for the tweets was between December 2020 until January 2021 and was based on the promotion sales commonly used by the organisation to boost their sales which is called Year End Sales. Corpus-based approach and Machine Learning model using RapidMiner were used in this study, namely, Support Vector Machine (SVM), Naïve Bayes, and Deep Learning. The corpus determines the sentiment from the tweets, either positive, negative, or neutral. The models' performances were compared in terms of accuracy, and the outcome shows that Deep Learning classifiers have the highest performance compared to other classifiers. The results of this sentiment analysis are visualised for easy understanding. © 2021. All Rights Reserved.
Science and Information Organization
2158107X
English
Article
All Open Access; Gold Open Access
author Rahim M.R.A.; Mahmud Y.; Abdul-Rahman S.
spellingShingle Rahim M.R.A.; Mahmud Y.; Abdul-Rahman S.
Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis
author_facet Rahim M.R.A.; Mahmud Y.; Abdul-Rahman S.
author_sort Rahim M.R.A.; Mahmud Y.; Abdul-Rahman S.
title Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis
title_short Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis
title_full Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis
title_fullStr Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis
title_full_unstemmed Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis
title_sort Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis
publishDate 2021
container_title International Journal of Advanced Computer Science and Applications
container_volume 12
container_issue 12
doi_str_mv 10.14569/IJACSA.2021.0121229
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122571438&doi=10.14569%2fIJACSA.2021.0121229&partnerID=40&md5=377254d254df1e8f4c6905b314405c90
description Mobile telecommunication companies in Malaysia have been widely used in the recent decade. There is intense competition among them to keep and gain new customers by offering various services. The reviews of the services by the customers are commonly shared on social media such as Twitter. Those reviews are essential for mobile telecommunication companies to improve their services and at the same time to keep their customers from churning to another company. Hence, this study focuses on the public sentiment on Twitter towards mobile telecommunication services in Malaysia. Data on Twitter was scraped using three keywords: Celcom, Digi, and Maxis. The keywords used to refer to Malaysia's top three mobile telecommunication companies. The timeline for the tweets was between December 2020 until January 2021 and was based on the promotion sales commonly used by the organisation to boost their sales which is called Year End Sales. Corpus-based approach and Machine Learning model using RapidMiner were used in this study, namely, Support Vector Machine (SVM), Naïve Bayes, and Deep Learning. The corpus determines the sentiment from the tweets, either positive, negative, or neutral. The models' performances were compared in terms of accuracy, and the outcome shows that Deep Learning classifiers have the highest performance compared to other classifiers. The results of this sentiment analysis are visualised for easy understanding. © 2021. All Rights Reserved.
publisher Science and Information Organization
issn 2158107X
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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